Description of the 3D AI model

Here we will introduce the features and technologies of the pioneering 3D AI that we are researching and developing.

What is CNN

Convolutional Neural Networks (CNNs) are often used in AI models that recognize images using deep learning technology.

This is because the amount of image data is large and it is difficult to calculate with a neural network unless it is compressed using CNN convolution and pooling processing.

The simple example below illustrates the procedure by which an image of 32 times 32, 1024 pixels is compressed into a 20-dimensional fully connected layer.

2D image CNN

Two-dimensional image convolution uses an array of pixels. Since the arrangement of pixels is fixed as shown in the figure below, it is possible to convolve with a filter of fixed shape and size.

CNN for 3D shape

The 3D shape recognition technology that we are researching and developing also requires the convolution technology as well as the 2D image.

A 3D shape is a 3D geometry created by a 3D CAD or 3D scanner.

Before the spread of 3D CAD, design data was two-dimensional drawing data, but now design data is stored as a three-dimensional shape with three dimensions of height, width, and depth, and is used for various purposes.

These three-dimensional shapes are data that have X, Y, and Z coordinate values ​​at each node (vertex) position.

By understanding the node position and the connection between the nodes, AI can recognize the features and dimensions of the shape.

3D shape convolution technology is used in the process of recognition by 3D AI.

Looking at the 3D shape data mentioned above in detail, the number of internode networks is not fixed as shown in the figure below, so the same fixed size filter as the 2D image cannot be used.

Our technology allows us to convolve this variable network structure, which allows AI to efficiently recognize 3D shapes.

Details of the network between nodes

Application of 3D AI

The developed 3D shape recognition AI model can be applied to functions such as classifying shapes into specified groups by recognizing their features, matching not only shapes but also dimensions, and synthesizing new shapes by mixing features of multiple shapes.